Current Issue : April - June Volume : 2017 Issue Number : 2 Articles : 5 Articles
In the past two decades, a significant number of innovative sensing and monitoring systems based on the machine vision-based\ntechnology have been exploited in the field of structural health monitoring (SHM). This technology has some inherent distinctive\nadvantages such as noncontact, nondestructive, long distance, high precision, immunity to electromagnetic interference, and\nlarge-range and multiple-target monitoring. A lot of machine vision-based structural dynamic measurement and structural state\ninspection methods have been proposed. Real-world applications are also carried out to measure the structural physical parameters\nsuch as the displacement, strain/stress, rotation, vibration, crack, and spalling. The purpose of this review article is devoted\nto presenting a summary of the basic theories and practical applications of the machine vision-based technology employed in\nstructural monitoring as well as its systematic error sources and integration with other modern sensing techniques....
Visual odometry estimates the ego-motion of an agent (e.g., vehicle and robot) using\nimage information and is a key component for autonomous vehicles and robotics. This paper\nproposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig\nwith optical flow analysis. An objective function fitted with a set of feature points is created by\nestablishing the mathematical relationship between optical flow, depth and camera ego-motion\nparameters through the cameraââ?¬â?¢s 3-dimensional motion and planar imaging model. Accordingly,\nthe six motion parameters are computed by minimizing the objective function, using the iterative\nLevenbergââ?¬â??Marquard method. One of key points for visual odometry is that the feature points\nselected for the computation should contain inliers as much as possible. In this work, the feature\npoints and their optical flows are initially detected by using the Kanadeââ?¬â??Lucasââ?¬â??Tomasi (KLT)\nalgorithm. A circle matching is followed to remove the outliers caused by the mismatching of\nthe KLT algorithm. A space position constraint is imposed to filter out the moving points from the\npoint set detected by the KLT algorithm. The Random Sample Consensus (RANSAC) algorithm is\nemployed to further refine the feature point set, i.e., to eliminate the effects of outliers. The remaining\npoints are tracked to estimate the ego-motion parameters in the subsequent frames. The approach\npresented here is tested on real traffic videos and the results prove the robustness and precision of\nthe method....
In this study, a visual grading system of vegetable grafting machine was developed. The study described key technology of\nvisual grading system of vegetable grafting machine. First, the contrasting experiment was conducted between acquired\nimages under blue background light and natural light conditions, with the blue background light chosen as lighting source.\nThe Visual C++ platform with open-source computer vision library (Open CV) was used for the image processing.\nSubsequently, maximum frequency of total number of 0-valued pixels was predicted and used to extract the measurements\nof scion and rootstock stem diameters. Finally, the developed integrated visual grading system was experimented\nwith 100 scions and rootstock seedlings. The results showed that success rate of grading reached up to 98%. This shows\nthat selection and grading of scion and rootstock could be fully automated with this developed visual grading system.\nHence, this technology would be greatly helpful for improving the grading accuracy and efficiency....
This paper presents a normalized human height estimation algorithm using an uncalibrated camera. To estimate the normalized\nhuman height, the proposed algorithm detects a moving object and performs tracking-based automatic camera calibration. The\nproposed method consists of three steps: (i) moving human detection and tracking, (ii) automatic camera calibration, and (iii)\nhuman height estimation and error correction.The proposed method automatically calibrates camera by detecting moving humans\nand estimates the human height using error correction. The proposed method can be applied to object-based video surveillance\nsystems and digital forensic....
Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in\npedestrian tracking for nonlinear and non-Gaussian estimation problems.However, pedestrian many problems due to changes of pedestrian postures and scale,moving background,mutual occlusion, and presence of\npedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in\nvideo sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge\nthus to achieve multi pedestrian detection; it adopts color and texture features into particle filter to get better observation results and\nthen automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking,\nthe algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates\ndetection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust\ntracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm\nimproves the tracking performance and has better tracking results....
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